Mark J. Schervish

27 papers A 3Journal 18Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	—	`conf`	Elicitation for sets of probabilities and distributions. ISIPTA Mark J. Schervish, Teddy Seidenfeld, Ruobin Gong, Joseph B. Kadane, Rafael Bassi Stern
2025	J	`jnl`	When no Price is Right. Rev. Symb. Log. Mark J. Schervish, Teddy Seidenfeld, Joseph B. Kadane, Ruobin Gong, Rafael Bassi Stern
2022	J	`jnl`	Learning and total evidence with imprecise probabilities. Int. J. Approx. Reason. Ruobin Gong, Joseph B. Kadane, Mark J. Schervish, Teddy Seidenfeld, Rafael Bassi Stern
2021	—	`conf`	Total Evidence and Learning with Imprecise Probabilities. ISIPTA Ruobin Gong, Joseph B. Kadane, Mark J. Schervish, Teddy Seidenfeld
2017	J	`jnl`	Nonconglomerability for countably additive Measures that are not κ-additive. Rev. Symb. Log. Mark J. Schervish, Teddy Seidenfeld, Joseph B. Kadane
2017	J	`jnl`	Nonparametric Risk Bounds for Time-Series Forecasting. J. Mach. Learn. Res. Daniel J. McDonald, Cosma Rohilla Shalizi, Mark J. Schervish
2014	J	`jnl`	On the equivalence of conglomerability and disintegrability for unbounded random variables. Stat. Methods Appl. Mark J. Schervish, Teddy Seidenfeld, Joseph B. Kadane
2012	J	`jnl`	Forecasting with imprecise probabilities. Int. J. Approx. Reason. Teddy Seidenfeld, Mark J. Schervish, Joseph B. Kadane
2012	J	`jnl`	Time series forecasting: model evaluation and selection using nonparametric risk bounds CoRR Daniel J. McDonald, Cosma Rohilla Shalizi, Mark J. Schervish
2011	J	`jnl`	Estimating beta-mixing coefficients CoRR Daniel J. McDonald, Cosma Rohilla Shalizi, Mark J. Schervish
2011	A	`conf`	Estimating beta-mixing coefficients. AISTATS Daniel J. McDonald, Cosma Rohilla Shalizi, Mark J. Schervish
2011	J	`jnl`	Generalization error bounds for stationary autoregressive models CoRR Daniel J. McDonald, Cosma Rohilla Shalizi, Mark J. Schervish
2011	J	`jnl`	Risk bounds for time series without strong mixing CoRR Daniel J. McDonald, Cosma Rohilla Shalizi, Mark J. Schervish
2010	J	`jnl`	Coherent choice functions under uncertainty. Synth. Teddy Seidenfeld, Mark J. Schervish, Joseph B. Kadane
2009	J	`jnl`	Proper Scoring Rules, Dominated Forecasts, and Coherence. Decis. Anal. Mark J. Schervish, Teddy Seidenfeld, Joseph B. Kadane
2008	J	`jnl`	The fundamental theorems of prevision and asset pricing. Int. J. Approx. Reason. Mark J. Schervish, Teddy Seidenfeld, Joseph B. Kadane
2003	—	`conf`	Extensions of Expected Utility Theory and Some Limitations of Pairwise Comparisons. ISIPTA Mark J. Schervish, Teddy Seidenfeld, Joseph B. Kadane, Isaac Levi
2003	—	`conf`	Nonstationary Covariance Functions for Gaussian Process Regression. NIPS Christopher J. Paciorek, Mark J. Schervish
2003	J	`jnl`	Path Planning for Robotic Demining: Robust Sensor-based Coverage of Unstructured Environments and Probabilistic Methods. Int. J. Robotics Res. Ercan U. Acar, Howie Choset, Yangang Zhang, Mark J. Schervish
2002	J	`jnl`	An Efficient Sampling Technique for Bayesian Inference With Computationally Demanding Models. Technometrics Peter Reichert, Mark J. Schervish, Mitchell J. Small
2002	A	`conf`	Probabilistic hierarchical spatial model for mine locations and its application in robotic landmine search. IROS Yangang Zhang, Mark J. Schervish, Howie Choset
2001	A	`conf`	Probabilistic methods for robotic landmine search. IROS Yangang Zhang, Mark J. Schervish, Ercan U. Acar, Howie Choset
2000	J	`jnl`	How Sets of Coherent Probabilities May Serve as Models for Degrees of Incoherence. Int. J. Uncertain. Fuzziness Knowl. Based Syst. Mark J. Schervish, Teddy Seidenfeld, Joseph B. Kadane
2000	—	`conf`	Probabilistic methods for robotic landmine search. Mobile Robots / Telemanipulator and Telepresence Technologies Yangang Zhang, Mark J. Schervish, Ercan U. Acar, Howie Choset
1999	—	`conf`	How Sets of Coherent Probabilities may Serve as Models for Degrees of Incoherence. ISIPTA Mark J. Schervish, Teddy Seidenfeld, Joseph B. Kadane
1995	J	`jnl`	How Many Comparisons Does Quicksort Use? J. Algorithms William F. Eddy, Mark J. Schervish
1990	J	`jnl`	Two perspectives on consensus for (Bayesian) inference and decisions. IEEE Trans. Syst. Man Cybern. Teddy Seidenfeld, Mark J. Schervish